A genomic copy number signature predicts radiation exposure in post-Chernobyl breast cancer

Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data

Despite intensive basic scientific, translational, and clinical efforts in the last decades, glioblastoma remains a devastating disease with a highly dismal prognosis. Apart from the implementation of temozolomide into the clinical routine, novel treatment approaches have largely failed, emphasizing the need for systematic examination of glioblastoma therapy resistance in order to identify major drivers and thus, potential vulnerabilities for therapeutic intervention. Recently, we provided proof-of-concept for the systematic identification of combined modality radiochemotherapy treatment vulnerabilities via integration of clonogenic survival data upon radio(chemo)therapy with low-density transcriptomic profiling data in a panel of established human glioblastoma cell lines. Here, we expand this approach to multiple molecular levels, including genomic copy number, spectral karyotyping, DNA methylation, and transcriptome data. Correlation of transcriptome data with inherent therapy resistance on the single gene level yielded several candidates that were so far underappreciated in this context and for which clinically approved drugs are readily available, such as the androgen receptor (AR). Gene set enrichment analyses confirmed these results, and identified additional gene sets, including reactive oxygen species detoxification, mammalian target of rapamycin complex 1 (MTORC1) signaling, and ferroptosis/autophagy-related regulatory circuits to be associated with inherent therapy resistance in glioblastoma cells. To identify pharmacologically accessible genes within those gene sets, leading edge analyses were performed yielding candidates with functions in thioredoxin/peroxiredoxin metabolism, glutathione synthesis, chaperoning of proteins, prolyl hydroxylation, proteasome function, and DNA synthesis/repair. Our study thus confirms previously nominated targets for mechanism-based multi-modal glioblastoma therapy, provides proof-of-concept for this workflow of multi-level data integration, and identifies novel candidates for which pharmacological inhibitors are readily available and whose targeting in combination with radio(chemo)therapy deserves further examination. In addition, our study also reveals that the presented workflow requires mRNA expression data, rather than genomic copy number or DNA methylation data, since no stringent correlation between these data levels could be observed. Finally, the data sets generated in the present study, including functional and multi-level molecular data of commonly used glioblastoma cell lines, represent a valuable toolbox for other researchers in the field of glioblastoma therapy resistance.

Increased replication stress and R-loop accumulation in EGFRvIII-expressing glioblastoma present new therapeutic opportunities

Background

The oncogene epidermal growth factor receptor variant III (EGFRvIII) is expressed in approximately one-third of all glioblastomas (GBMs). So far it is not clear if EGFRvIII expression induces replication stress in GBM cells, which might serve as a therapeutical target.

Methods

Isogenetic EGFRvIII− and EGFRvIII+ cell lines with endogenous EGFRvIII expression were used. Markers of oncogenic and replication stress such as γH2AX, RPA, 53BP1, ATR, and CHK1 were analyzed using western blot, immunofluorescence, and flow cytometry. The DNA fiber assay was performed to analyze replication, transcription was measured by incorporation of EU, and genomic instability was investigated by micronuclei and CGH-Array analysis. Immunohistochemistry staining was used to detect replication stress markers and R-loops in human GBM samples.

Results

EGFRvIII+ cells exhibit an activated replication stress response, increased spontaneous DNA damage, elevated levels of single-stranded DNA, and reduced DNA replication velocity, which are all indicative characteristics of replication stress. Furthermore, we show here that EGFRvIII expression is linked to increased genomic instability. EGFRvIII-expressing cells display elevated RNA synthesis and R-loop formation, which could also be confirmed in EGFRvIII-positive GBM patient samples. Targeting replication stress by irinotecan resulted in increased sensitivity of EGFRvIII+ cells.

Conclusion

This study demonstrates that EGFRvIII expression is associated with increased replication stress, R-loop accumulation, and genomic instability. This might contribute to intratumoral heterogeneity but may also be exploited for individualized therapy approaches.

Landscape of Immune Microenvironment Under Immune Cell Infiltration Pattern in Breast Cancer

Background

Increasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy.

Methods

Multiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the “maftools” R package.

Results

Three different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed.

Conclusion

This work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.

Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study

Background

Prognostic models based on high-dimensional omics data generated from clinical patient samples, such as tumor tissues or biopsies, are increasingly used for prognosis of radio-therapeutic success. The model development process requires two independent discovery and validation data sets. Each of them may contain samples collected in a single center or a collection of samples from multiple centers. Multi-center data tend to be more heterogeneous than single-center data but are less affected by potential site-specific biases. Optimal use of limited data resources for discovery and validation with respect to the expected success of a study requires dispassionate, objective decision-making. In this work, we addressed the impact of the choice of single-center and multi-center data as discovery and validation data sets, and assessed how this impact depends on the three data characteristics signal strength, number of informative features and sample size.

Methods

We set up a simulation study to quantify the predictive performance of a model trained and validated on different combinations of in silico single-center and multi-center data. The standard bioinformatical analysis workflow of batch correction, feature selection and parameter estimation was emulated. For the determination of model quality, four measures were used: false discovery rate, prediction error, chance of successful validation (significant correlation of predicted and true validation data outcome) and model calibration.

Results

In agreement with literature about generalizability of signatures, prognostic models fitted to multi-center data consistently outperformed their single-center counterparts when the prediction error was the quality criterion of interest. However, for low signal strengths and small sample sizes, single-center discovery sets showed superior performance with respect to false discovery rate and chance of successful validation.

Conclusions

With regard to decision making, this simulation study underlines the importance of study aims being defined precisely a priori. Minimization of the prediction error requires multi-center discovery data, whereas single-center data are preferable with respect to false discovery rate and chance of successful validation when the expected signal or sample size is low. In contrast, the choice of validation data solely affects the quality of the estimator of the prediction error, which was more precise on multi-center validation data.

Retrospective biodosimetry techniques: Focus on cytogenetics assays for individuals exposed to ionizing radiation

In the absence of physical data, biodosimetry tools are required for fast dose and risk assessment in the event of radiological or nuclear mass accidents or attacks to triage exposed humans and take immediate medical countermeasures. Biodosimetry tools have mostly been developed for retrospective dose assessment and the follow-up of victims of irradiation. Among them, cytogenetics analyses, to reveal chromosome damage, are the most developed and allow the determination of doses from blood samples as low as 100 mGy. Various cytogenetic tests have already allowed retrospective dose assessment of Chernobyl liquidators and military personnel exposed to nuclear tests after decades. In this review, we discuss the properties of various biodosimetry techniques, such as their sensitivity and limitations as a function of the time from exposure, using multiple examples of nuclear catastrophes or working exposure. Among them, chromosome FISH hybridization, which reveals chromosome translocations, is the most reliable due to the persistence of translocations for decades, whereas dicentric chromosome and micronuclei assays allow rapid and accurate dose assessment a short time after exposure. Both need to be adjusted through mathematical algorithms for retrospective analyses, accounting for the time since exposure and the victims' age. The goal for the future will be to better model chromosome damage, reduce the time to result, and develop new complementary biodosimetry approaches, such as mutation signatures.

Establishing mechanisms affecting the individual response to ionizing radiation

Purpose: Humans are increasingly exposed to ionizing radiation (IR). Both low (<100 mGy) and high doses can cause stochastic effects, including cancer; whereas doses above 100 mGy are needed to promote tissue or cell damage. 10-15% of radiotherapy (RT) patients suffer adverse reactions, described as displaying radiosensitivity (RS). Sensitivity to IR's stochastic effects is termed radiosusceptibility (RSu). To optimize radiation protection we need to understand the range of individual variability and underlying mechanisms. We review the potential mechanisms contributing to RS/RSu focusing on RS following RT, the most tractable RS group.Conclusions: The IR-induced DNA damage response (DDR) has been well characterized. Patients with mutations in the DDR have been identified and display marked RS but they represent only a small percentage of the RT patients with adverse reactions. We review the impacting mechanisms and additional factors influencing RS/RSu. We discuss whether RS/RSu might be genetically determined. As a recommendation, we propose that a prospective study be established to assess RS following RT. The study should detail tumor site and encompass a well-defined grading system. Predictive assays should be independently validated. Detailed analysis of the inflammatory, stress and immune responses, mitochondrial function and life style factors should be included. Existing cohorts should also be optimally exploited.

DNA Copy Number Variations as Markers of Mutagenic Impact

DNA copy number variation (CNV) occurs due to deletion or duplication of DNA segments resulting in a different number of copies of a specific DNA-stretch on homologous chromosomes. Implications of CNVs in evolution and development of different diseases have been demonstrated although contribution of environmental factors, such as mutagens, in the origin of CNVs, is poorly understood. In this review, we summarize current knowledge about mutagen-induced CNVs in human, animal and plant cells. Differences in CNV frequencies induced by radiation and chemical mutagens, distribution of CNVs in the genome, as well as adaptive effects in plants, are discussed. Currently available information concerning impact of mutagens in induction of CNVs in germ cells is presented. Moreover, the potential of CNVs as a new endpoint in mutagenicity test-systems is discussed.

Demographic and onco-epidemiological situation in radioactive contaminated territory of Zhytomyr Oblast

We performed an assessment of demographic parameters of occurrence of malignant tumours and mortality of the population which lives in the radioactive contaminated territory of Zhytomyr Oblast (Yemilchynskyi, Luhynskyi, Narodytskyi, Korostenskyi, Olevskyi, and Ovrutskyi districts) over a 32-year period (1985–2017). The source material for the study of the demographic situation and malignant tumours in the population of the radioactive contaminated administrative districts of Zhytomyr Oblast during 1985–2017 was the statistical data of the Management of Healthcare of Zhytomyr Oblast State Administration, Central Department of Statistics in Zhytomyr Oblasts, reports on occurrences of malignant tumours of the state institution Center of Medical Statistics of the Ministry of Healthcare of Ukraine, data of the Radiological Control Service in Zhytomyr Oblast. It was determined that over 1985–2017 in the radioactive contaminated territory of Zhytomyr Oblast, a natural decline of population was observed, maximum values of which occurred in 2005 (except Narodnytsky district – 2000). The highest levels of occurrence of tumours and mortality caused by them among the adult population were observed 14 years after the Chornobyl nuclear power plant disaster. The peak of the occurrence of malignant tumours among children in the radioactive contaminated territory of Zhytomyr Oblast was observed 9 years after the explosion. A relationship was determined between doses of irradiation of the population and the risk of occurrence of malignant tumours in the radioactive contaminated regions of Zhytomyr Oblast. Taking into account that the method of calculation of passport dose (average annual effective dose of irradiation) was developed 23 years ago, nowadays it can lead to errors. This is related to change in amounts of consumption of different products by the population, which needs to be considered when determining radiological risks. The obtained data indicate that analysis of demographic and onco-epidemiological consequences of the Chornobyl catastrophe remains a relevant issue nowadays and will remain so in the near future.